Attention-based Group Recommendation

Group recommendation aims to recommend items for a group of users, e.g., recommending a restaurant for a group of colleagues. The group recommendation problem is challenging, in that a good model should understand the group decision making process appropriately: users are likely to follow decisions of only a few users, who are group's leaders or experts. To address this challenge, we propose using an attention mechanism to capture the impact of each user in a group. Specifically, our model learns the influence weight of each user in a group and recommends items to the group based on its members' weighted preferences. Moreover, our model can dynamically adjust the weight of each user across the groups; thus, the model provides a new and flexible method to model the complicated group decision making process, which differentiates us from other existing solutions. Through extensive experiments, it has demonstrated that our model significantly outperforms baseline methods for the group recommendation problem.

Cross Device Matching for Online Advertising with Neural Feature Ensembles : First Place Solution at CIKM Cup 2016

We describe the 1st place winning approach for the CIKM Cup 2016 Challenge. In this paper, we provide an approach to reasonably identify same users across multiple devices based on browsing logs. Our approach regards a candidate ranking problem as pairwise classification and utilizes an unsupervised neural feature ensemble approach to learn latent features of users. Combined with traditional hand crafted features, each user pair feature is fed into a supervised classifier in order to perform pairwise classification. Lastly, we propose supervised and unsupervised inference techniques.